Association between profiles of accelerometer-measured daily movement behaviour and mortality risk: a prospective cohort study of British older adults

Objectives We identified profiles of wake-time movement behaviours (sedentary behaviours, light intensity physical activity and moderate-to-vigorous physical activity) based on accelerometer-derived features among older adults and then examined their association with all-cause mortality. Methods Data were drawn from a prospective cohort of 3991 Whitehall II accelerometer substudy participants aged 60–83 years in 2012–2013. Daily movement behaviour profiles were identified using k-means cluster analysis based on 13 accelerometer-assessed features characterising total duration, frequency, bout duration, timing and activity intensity distribution of movement behaviour. Cox regression models were used to assess the association between derived profiles and mortality risk. Results Over a mean follow-up of 8.1 (SD 1.3) years, a total of 410 deaths were recorded. Five distinct profiles were identified and labelled as ‘active’ (healthiest), ‘active sitters’, ‘light movers’, ‘prolonged sitters’, and ‘most sedentary’ (most deleterious). In model adjusted for sociodemographic, lifestyle, and health-related factors, compared with the ‘active’ profile, ‘active sitters’ (HR 1.57, 95% CI 1.01 to 2.44), ‘light movers’ (HR 1.75, 95% CI 1.17 to 2.63), ‘prolonged sitters’ (HR 1.67, 95% CI 1.11 to 2.51), ‘most sedentary’ (HR 3.25, 95% CI 2.10 to 5.02) profiles were all associated with a higher risk of mortality. Conclusion Given the threefold higher mortality risk among those with a ‘most sedentary’ profile, public health interventions may target this group wherein any improvement in physical activity and sedentary behaviour might be beneficial.


Covariates
Covariates were assessed by questionnaire or at clinical examination during 2012 -2013 wave of data collection, as well as from electronic health records including HES and the Mental Health Services dataset.Sociodemographic variables consisted of sex, ethn icity (white, non-white), marital status (married/cohabitating, divorced/widowed/single), education (≤primary school, lower secondary, higher secondary school, university, higher degree; treated as a continuous variable), and last known occupational position (high, intermediate, low).Lifestyle factors consisted of fruit and vegetable consumption (less than once daily, once daily, more than once daily), smoking status (current and recent ex-(less than 5 years) smokers, long term ex-smokers, never smokers), and alcohol consumption (0, 1-14, >14 units per week).Health-related factors comprised cardiometabolic factors and a morbidity index.Cardiometabolic factors included body mass index (BMI; categorized as <24.9, 25 -29.9 and ≥30 kg/m2), prevalent diabetes (fasting glucose ≥7.0 mmol/l or self-reported doctor diagnosis or use of diabetes medication or hospitalizations ascertained through record linkage to HES (ICD -9 codes 250 or ICD-10 code E11), hypertension (systolic/diastolic blood pressure ≥140/90 mmHg or use of antihypertensive drugs), and hyperlipidaemia (low-density lipoproteins (LDL) >4.1 mmol/l or use of lipid-lowering drugs).A morbidity index was calculated as the number of the following chronic conditions: coronary heart disease, stroke, heart failure, cancer, arthritis, chronic obstructive pulmonary disease, depression, Parkinson disease, and dementia.
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Determination of the range of possible number of profiles and software/package used for the analysis
The user must specify the number of profiles (k) in the k-means clustering approach.It is recommended to combine the findings of many procedures rather than relying on a single rule to determine the number of profiles (clusters). 1 As a result, two different indices were utilized to determine the appropriate range of number of clusters to be examined.
A) The Elbow method selects the number of clusters to be such that adding an additional cluster does not significantly reduce the within-group sum of squares, which quantifies the degree to which items within a cluster are similar, representing a trade-off between a reasonable number of clusters and clustering quality.B) Gap statistic compares the clusters created from the observed data and clusters created from a randomly generated dataset, known as the reference dataset.For a given k, the gap statistic is the difference in the total within-cluster variance for the observed data and that of the reference dataset.
The optimal number of clusters is denoted by the value of k that yields top the largest gap statistic. 2uster analysis was undertaken in R (version 3.6.1,http://www.r-project.org/)using the kmeans()  Mean value of 0 corresponds to the average observed value in the study population.Positive values represent higher acceleration, higher total duration in SB, LIPA and MVPA, higher number of bouts, higher mean duratio n of bouts, higher intensity gradient, higher intensity constant, and later timing of activity.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s)  A) The Elbow method selects the number of clusters to be such that adding an additional cluster does not significantly reduce the within-group sum of squares, which quantifies the degree to which items within a cluster are similar, representing a trade-off between a reasonable number of clusters and clustering quality.B) Gap statistic method compares the clusters created from the observed data and clusters created from a randomly generated dataset, known as the reference dataset.For a given k, the gap statistic is the difference in the total within-cluster variance for the observed data and that of the reference dataset.The optimal number of clusters is denoted by the value of k that yields the largest gap statistic.
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eFigure 2
Determination of the optimal number of clusters A) Using the Elbow method B) Using the Gap statistic method

Hazard ratio (95% confidence interval) Daily movement behaviour features Model adjusted for sociodemographic factors*
Association between each feature of daily movement behaviour and all-cause mortality (N total = 3991, N cases = 410, mean follow-up (standard deviation) = 8.1 (1.3) years) Parameters assessing the variability of participants within and between clusters for different profiles Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) Standardized values of movement behaviour variables by profiles Abbreviations: M, mean; SD, standard deviation.*Modelsadjustedforage(timescale), sex, ethnicity, marital status, education, and last occupational position.†Modelsadditionallyadjustedforsmoking status, alcohol consumption, and fruit and vegetable consumption.‡Modelsadditionallyadjustedfor body mass index, hypertension, hyperlipidaemia, diabetes, and morbidity index.Abbreviations: LIPA, light intensity physical activity; MVPA, moderate-to-vigorous activity; SB, sedentary behaviour.Abbreviations: LIPA, light intensity physical activity; MVPA, moderate-to-vigorous physical activity; SB, sedentary behaviour.BMJ Association between profiles of movement behaviours and all-cause mortality, with different reference categories (N total = 3991, N cases = 410, mean follow-up (SD) = 8.1 (1.3) years) All models adjusted for age (time-scale), sex, ethnicity, marital status, education, last occupational position, smoking status, alcohol consumption, fruit and vegetable consumption, body mass index, hypertension, hyperlipidaemia, diabetes, and morbidity index.Association between profiles of movement behaviours and all-cause mortality using 2-year washout period (N total = 3946, N cases = 365, mean follow-up (SD) = 6.2 (1.04) years) BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) **Models adjusted for age (time-scale), sex, ethnicity, marital status, education, and last occupational position.†Modelsadditionally adjusted for smoking status, alcohol consumption , and fruit and vegetable consumption.‡Modelsadditionally adjusted for body mass index, hypertension, hyperlipidaemia, diabetes, and morbidity index..